4.7 Article

Spatiotemporal variations and gradient functions of water turbidity in shallow lakes

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ECOLOGICAL INDICATORS
卷 147, 期 -, 页码 -

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ELSEVIER
DOI: 10.1016/j.ecolind.2023.109928

关键词

Water quality; Gradient analysis; Curve fitting; Risk area; Risk assessment

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Shallow lakes are difficult to manage due to their proneness to water quality deterioration. Turbidity, a commonly used parameter, can be used to estimate water quality. This study conducted continuous field observations and monitoring of turbidity in Baoan Lake, China, and identified the risk levels of water turbidity using gradient analysis. The results showed that turbidity varied seasonally, with higher values in summer and autumn and lower values in winter. The methodology proposed in this study provides a new tool for quantifying spatial gradient variation of water turbidity and determining high-risk areas and risk levels.
Shallow lakes are prone to water quality deterioration and are difficult to manage. Turbidity is a physical parameter commonly used to estimate water quality. Revealing the spatiotemporal variations in turbidity can help determine the risk areas of water turbidity to achieve efficient protection and management of water re-sources. Here, we conducted continuous field observations and monitoring of turbidity in Baoan Lake (114 degrees 39 '-114 degrees 46 ' E, 30 degrees 12 '-30 degrees 18 ' N), a shallow lake (average depth: 2.27 m) in southeastern Hubei Province, China, from July 2019 to May 2022, to perform gradient analysis and determine risk levels of water turbidity. Results showed that the average turbidity of the Baoan Lake varied within the range of 9.0-48.8 NTU. Water turbidity fluctuated, reaching peaks in summer or autumn, and troughs in winter. The overall variation of water turbidity in spring and summer ranged within 15 NTU, while in autumn and winter it was over 29 NTU. The turbidity of Zhuti and Qiaodun Lakes (ZL and QL, respectively) was often higher than that of Xiaosihai and Biandantang Lakes (XL and BL, respectively), showing a turbidity decrease from southeast to northwest, espe-cially in winter. A gradient function describing the spatial variation in turbidity was summarized. The parameters of the function had implicit meanings for spatial variations in turbidity. Parameter a influenced the form of the fitting curve. Parameters b and c reflected the range of turbidity values. The two points of the second derivative of the function were considered to indicate the radius of the risk area in a concentric manner. The methodology proposed to identify the risk levels of water turbidity entails the calculation of the risk values for different seasons. Thus, this study provides a new tool for quantifying spatial gradient variation of water turbidity and a new method for determining high-risk areas and risk levels of water turbidity.

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